code: def test_action_repeats_successfully_apply_repeated_action(self):
      action_repeat = 5
      control_env = self.create_env()
      gym_env = deepmind_control_lib.DeepmindControlPreprocessing(
          control_env, action_repeat=action_repeat
      )
  
      gym_env.reset()
  
      # Step the environment until it is two steps away from being a game over.
      for _ in range(self.max_steps // action_repeat - 2):
          gym_env.step(gym_env.action_space.sample())
      gym_env.step(gym_env.action_space.sample())
      is_penultimate_game_over = gym_env.game_over
      gym_env.step(gym_env.action_space.sample())
      is_final_game_over = gym_env.game_over
  
      # We will only apply the required number of steps to reach a game over
      # if actions are being repeated as specified.
      self.assertFalse(is_penultimate_game_over)
      self.assertTrue(is_final_game_over)
  
imports: import collections
  from typing import Callable, Mapping
  from unittest import mock
  from absl.testing import absltest
  from absl.testing import parameterized
  from dm_control import suite
  from dm_control.rl import control
  import dm_env
  from dm_env import specs
  from dopamine.labs.sac_from_pixels import deepmind_control_lib
  import numpy
label_desc: Use image node_id 7 for calling the DeepmindControlLibTest obj's underlying member method code with example usage: obj.test_action_repeats_successfully_apply_repeated_action() without return types